# Algoscale

> Algoscale is an enterprise data, AI, and analytics consulting firm. We build production-grade data platforms, ship AI applications, and consolidate data estates across AWS, Azure, and Microsoft Fabric. Our proprietary S.C.A.L.E.™ accelerator stands up enterprise data warehouses and lakehouses in weeks, not quarters.

## Platforms and accelerators
- [The Enterprise Data Journey](https://algoscale.com/go/data-journey-for-enterprise/): A maturity-model view of how enterprises move from scattered reports to AI-native operations — and the specific work required at each stage.
- [Microsoft Fabric for Logistics & Transportation](https://algoscale.com/go/fabric-logistics/): Algoscale builds Microsoft Fabric data warehouses for carriers, 3PLs, and shippers - with TMS/WMS/ELD unified, role-specific KPIs, and RBAC.
- [Microsoft Fabric for Manufacturing](https://algoscale.com/go/fabric-manufacturing/): Algoscale builds Microsoft Fabric data warehouses for manufacturers - with role-specific KPIs, RBAC, certified metrics, and governance in weeks.
- [Microsoft Fabric Migration](https://algoscale.com/go/fabric-migration/): Move your analytics estate to Microsoft Fabric without breaking what works. A staged, governed, cost-aware migration from Synapse, Databricks, and Power BI.
- [Hybrid Cloud: Azure + AWS Challenges](https://algoscale.com/go/hybrid-cloud-azure-aws-challenges/): Six real challenges of running Azure and AWS side by side — and a four-step playbook to stop bleeding cost, latency, and engineering time.
- [Post-Acquisition Data Integration](https://algoscale.com/go/m-and-a-consolidation/): Consolidate acquired ERPs, CRMs, and warehouses into one source of truth. Algoscale's M&A data integration accelerator runs in months, not years.
- [S.C.A.L.E.™ - Enterprise Data Platform Accelerator](https://algoscale.com/go/scale/): S.C.A.L.E.™ is Algoscale's Terraform-driven enterprise data platform and lakehouse accelerator. Deploy a production data lake on AWS or Azure in weeks.

## Core data services
- [Data Warehouse Consulting](https://algoscale.com/data-warehouse-consulting-services/): Enterprise data warehouse modernization on AWS, Azure, or Microsoft Fabric
- [Data Lake Consulting](https://algoscale.com/data-lake-consulting-services/): Governed lakehouse deployments with Lake Formation, Unity Catalog, and Purview
- [Data Architecture Services](https://algoscale.com/data-architecture-services/): Multi-cloud data architecture strategy and target-state design
- [Data Engineering](https://algoscale.com/data-engineering-services/): Production data pipelines, CDC, streaming, and reverse ETL
- [Data Integration Consulting](https://algoscale.com/data-integration-consulting/): ERP, CRM, and SaaS integration at enterprise scale
- [Data Governance](https://algoscale.com/data-governance-consulting-services/): HIPAA, PCI-DSS, SOX, GDPR, and ISO 27001 enforcement at the infrastructure layer
- [Data Management](https://algoscale.com/data-management-services/): Master data management and enterprise data stewardship
- [Data Strategy Consulting](https://algoscale.com/data-strategy-consulting-services/): Data strategy, roadmap, and target operating model

## AI and applied ML
- [AI as a Service](https://algoscale.com/ai-as-a-service/): Enterprise AI deployment at scale
- [AI Consulting](https://algoscale.com/ai-consulting-service/): AI strategy, roadmap, and use-case prioritization
- [Generative AI Consulting](https://algoscale.com/generative-ai-consulting-services/): LLM application development and RAG architectures
- [AI Agent Development](https://algoscale.com/ai-agent-development-company/): Arcastra™-powered agentic applications
- [Data Science Consulting](https://algoscale.com/data-science-consulting-services/): ML model development and deployment
- [Predictive Analytics](https://algoscale.com/predictive-data-analytics-services/): Forecasting and predictive modeling for business operations
- [Analyst IQ](https://algoscale.com/analyst-iq/): Self-service analytical agent for internal teams
- [Voice AI Agent](https://algoscale.com/voice-ai-agent/): Voice AI agent on Arcastra™
- [Chat AI Agent](https://algoscale.com/chat-ai-agent/): Chat AI agent on Arcastra™

## Microsoft ecosystem
- [Microsoft Fabric Consulting](https://algoscale.com/microsoft-fabric-consulting/): Fabric implementation, Synapse migration, and OneLake patterns
- [Microsoft Power BI](https://algoscale.com/microsoft-power-bi-consulting-services/): Enterprise Power BI deployments, RLS, and semantic modeling
- [Microsoft Azure Development](https://algoscale.com/microsoft-azure-development-services/): Azure-native data platform, analytics, and app services

## BI and analytics
- [Business Intelligence Consulting](https://algoscale.com/business-intelligence-consulting-services/): BI strategy and implementation
- [Tableau Consulting](https://algoscale.com/tableau-consulting-services/): Tableau migrations, server deployments, and enterprise rollouts
- [Data Visualization](https://algoscale.com/data-visualization-consulting-services/): Dashboard design and executive reporting

## Cloud and modernization
- [Cloud Migration Services](https://algoscale.com/cloud-migration-services/): Enterprise cloud migration strategy and execution
- [Legacy Modernization](https://algoscale.com/legacy-modernization-services/): Mainframe and legacy system modernization
- [Cloud Application Development](https://algoscale.com/cloud-application-development-company/): Cloud-native application delivery
- [Digital Transformation](https://algoscale.com/digital-transformation-services/): End-to-end digital transformation programs

## Industry practices
- [BI for Healthcare](https://algoscale.com/business-intelligence/healthcare/): Business intelligence for healthcare providers and payers
- [Healthcare Software Development](https://algoscale.com/custom-healthcare-software-development-company/): HIPAA-compliant healthcare software development
- [BI for Finance](https://algoscale.com/business-intelligence/finance/): Business intelligence for banking, lending, and capital markets
- [Financial Software Development](https://algoscale.com/financial-software-development-services/): Financial services software and platform development
- [BI for Retail](https://algoscale.com/business-intelligence/retail/): Business intelligence for retail and CPG
- [Retail Software Development](https://algoscale.com/retail-software-development-services/): Retail software and POS integration
- [Retail Analytics Consulting](https://algoscale.com/retail-analytics-consulting/): Retail analytics, category performance, and customer LTV
- [Big Data in Manufacturing](https://algoscale.com/data/big-data-in-manufacturing/): Big data and analytics for manufacturing and industrial operations
- [Generative AI in Manufacturing](https://algoscale.com/generative-ai-in-manufacturing/): Generative AI use cases for manufacturing
- [BI for Insurance](https://algoscale.com/business-intelligence/insurance/): Business intelligence for insurance carriers and brokers
- [Insurance Software Development](https://algoscale.com/insurance-software-development-services/): Insurance software and claims platforms

## Tools
- [Data Maturity Assessment](https://algoscale.com/go/assessment/): Five-dimension self-assessment tool for enterprise data maturity
- [Engagement Calculator](https://algoscale.com/go/calculator/): Scope and timeline estimator for data and AI engagements

## Blog
The Algoscale Blog covers field notes from enterprise AI and data engagements: architecture choices, migrations, and hard-won production patterns.

- [Blog index](https://algoscale.com/go/blog/)
- [RSS feed](https://algoscale.com/go/blog/rss.xml)

### Published posts
- [The Multi-Brand Retail/CPG Data Foundation](https://algoscale.com/go/blog/retail-cpg-multi-brand-data-foundation/): Multi-banner retail groups and brand-house CPGs run N data estates pretending to be one. The data foundation that resolves identity, inventory, and pricing.
- [Data Lake Cost Optimization: 3 Levers](https://algoscale.com/go/blog/data-lake-cost-optimization/): Data lake cost optimization comes down to three levers: partition pruning, file compaction, lifecycle tiering. How to tune each one in production.
- [Insurance Claims Classification with LLMs](https://algoscale.com/go/blog/insurance-claims-llm-guardrails/): Triaging insurance claims across 61 labels needs more than a model — it needs frozen eval sets, per-label thresholds, and a lakehouse built for documents.
- [Multi-3PL Logistics Data Foundation](https://algoscale.com/go/blog/logistics-data-foundation-multi-3pl/): Running shipments across multiple 3PLs and dozens of carriers? Real network visibility starts at the data layer — the multi-3PL foundation pattern.
- [Manufacturing Data Foundation: A Blueprint](https://algoscale.com/go/blog/manufacturing-data-foundation-scale/): Plant floor, ERP, quality, and maintenance data live in four silos that never join. Here's the manufacturing data foundation architecture that fixes it.
- [Data Lake or Data Swamp: 3 Failure Modes](https://algoscale.com/go/blog/data-lake-that-doesnt-become-a-swamp/): Most data lakes drift into swamps within 18 months. A practitioner's breakdown of three failure modes — zones, governance, lifecycle — and the fixes.
- [Watermark Bugs in Fabric Incremental Loads](https://algoscale.com/go/blog/fabric-watermark-incremental-load-duplicates/): A watermark incremental load in Microsoft Fabric silently duplicated 3 months of Gold-layer data. The fix: idempotent MERGE plus a row-count assertion.
- [Beat NetSuite API Limits with SuiteQL](https://algoscale.com/go/blog/netsuite-suiteql-vs-rest-api-rate-limits/): Our NetSuite pipeline hit API rate limits and ran 28 hours per ingestion. Moving from the REST record API to SuiteQL cut it to under 6. Here's exactly how.
- [Lakehouse vs Warehouse vs Data Lake](https://algoscale.com/go/blog/lakehouse-vs-warehouse-vs-datalake-decision/): Lakehouse, warehouse, or data lake? A 2026 practitioner's decision framework that picks by workload concurrency, latency, team skill, and cost shape.
- [Retail Personalization Beyond the Carousel](https://algoscale.com/go/blog/retail-personalization-beyond-recommendations/): Most retail personalization stops at the recommendation carousel. The real lift lives in the inventory join and identity layer underneath.
- [Medallion Architecture: 5 Failure Modes](https://algoscale.com/go/blog/medallion-architecture-implementation-failures/): Most bronze/silver/gold lakehouse builds repeat the same five mistakes. A practitioner's breakdown of medallion architecture failure modes — and the fixes.
- [Iceberg vs Delta vs Hudi in 2026](https://algoscale.com/go/blog/iceberg-vs-delta-vs-hudi-2026/): After years of open table format wars, the 2026 picture is clear: Iceberg has won, but the catalog choice is now where vendor lock-in lives.
- [Supply Chain Visibility Beyond Dashboards](https://algoscale.com/go/blog/supply-chain-visibility-wms-tms-carrier/): Most supply chain visibility tools paint a dashboard over broken data. Real visibility lives in the WMS-TMS-carrier integration layer underneath.
- [Serverless MDM: Lambda + Postgres on AWS](https://algoscale.com/go/blog/serverless-mdm-pipeline-aws-lambda-postgres/): A production MDM pattern with Lambda + RDS PostgreSQL. Multi-ERP canonicalisation, ledger-hit caching, sub-50ms enrichment - without Profisee or Tamr.
- [Hybrid Row-Level Security: AWS + Power BI](https://algoscale.com/go/blog/hybrid-row-level-security-aws-power-bi/): How we wired Azure AD identities to AWS Lake Formation to Power BI - with row-level security that keeps field, regional, and exec reports distinct.
- [Post-Acquisition Data: The 180-Day Playbook](https://algoscale.com/go/blog/post-acquisition-data-consolidation-playbook/): Your acquisition closed. Your ERPs, CRMs, and data warehouses do not match. A 180-day playbook for consolidating the estate without the multi-year integration.
- [Why Predictive Maintenance Pilots Stall](https://algoscale.com/go/blog/predictive-maintenance-data-foundation/): Most enterprise predictive maintenance pilots stall before payback. The fix isn't more sensors — it's the data foundation underneath. Here's the pattern.
- [Fabric OneLake Shortcuts vs ADLS Mounts](https://algoscale.com/go/blog/fabric-shortcuts-vs-adls-mounts/): When OneLake shortcuts beat ADLS Gen2 mounts in Microsoft Fabric, when they silently break, and the decision matrix we use on every migration.
- [Microsoft Fabric vs Databricks, Honestly](https://algoscale.com/go/blog/microsoft-fabric-vs-databricks/): A practitioner's comparison of Fabric and Databricks across real enterprise workloads — with cost benchmarks and where each genuinely wins.
- [Synapse to Fabric: 4 Silent Breakages](https://algoscale.com/go/blog/synapse-to-fabric-migration-gotchas/): Four Synapse-to-Fabric migration gotchas that pass code review but break production: identity columns, distribution DDL, OPENROWSET, F-SKU throttling.
- [Why Your AI Pilot Stalls at 80%](https://algoscale.com/go/blog/why-ai-pilots-stall-at-80-percent/): Most enterprise AI pilots hit 80% accuracy in a demo and never reach production. Here's the data-stage failure pattern behind it — and a concrete path to ship.

## Case studies
- [Case studies index](https://algoscale.com/case-study/): Customer engagements across retail, manufacturing, fintech, insurance, and healthcare

## Company
- [About Algoscale](https://algoscale.com/about-algoscale-team/): Leadership team and company background
- [Careers](https://algoscale.com/careers/): Open roles at Algoscale
- [Contact](https://algoscale.com/contact/): Book an executive walkthrough or consultation